We study basic human decision making and learning processes when making repeated and/or sequential choice. Understanding the basic processes in these very common settings (e.g. driving, behavior in pandemics, using smartphone apps, health decisions) both improves our ability to predict behavior and to design mechanisms and policies that are robust to the likely behaviors of systems’ users.
We use big data on transactions in financial markets to discover evidence supporting psychological theories of decision making and use these psychological insights within machine learning systems to improve predictions of financial markets.
We integrate psychological theories and models of human decision making into machine learning systems to predict human decision making in state-of-the-art levels. Focusing on the most fundamental choice task from behavioral economics and using the largest datasets currently available, we study which theories and models, which types of machine learning algorithms and tools, and which methods of integration lead to the best out-of-sample predictions.
